Automatic picking of multi-modal Rayleigh-wave dispersion curves from multi-component data with an energy-density-based clustering method

Summary Rayleigh wave is widely used for characterizing shallow subsurface structures. The conventional Rayleigh-wave methods rely on the manual picking of dispersion curves, and the dispersion curves of multi-component data are usually merged manually. The manual processing of multi-component Rayleigh waves reduces the efficiency of the method, especially when the data size and the number of modes are large. To overcome these limitations, we develop an energy-based clustering method, namely the Energy-Density-Based Spatial Clustering of Applications with Noise (E-DBSCAN) algorithm. The E-DBSCAN algorithm extracts energy clusters and dispersion curves from a single dispersion image. It considers the dispersion-energy values of the surface wave and is able to pick the dispersion curve more reliably compared with the conventional DBSCAN algorithm. We propose a two-step clustering approach for the automatic picking of multi-mode dispersion curves from multi-component data: we first extract the energy clusters in the dispersion spectra of horizontal- and vertical-component data using E-DBSCAN, respectively, and combine them in the frequency-velocity domain. Then we extract multi-modal dispersion curves from the combined multi-component energy clusters with E-DBSCAN or DBSCAN. Numerical results show that our proposed method has fairly high accuracy and estimates more abundant multi-modal dispersion curves than the single-component method. Two field examples, including an active-source and an ambient-noise dataset, prove the validity of our method and the outperformance of multi-component results compared with the single-component results. Our proposed method has a relatively low dependence on parameter selection and is also applicable to multi-offset data, which is valuable for picking multi-modal dispersion curves.